This article introduces a new crop prediction method using hybrid machine learning (ML) and deep learning (DL) models. The proposed model comprises four phases: data preprocessing, feature fusion, feature selection, and prediction. Initially, the dataset is built with the information collected by 250 sensors located at different places in Maharashtra. The constructed dataset has provided sample data for 31 crops, each with four attributes: temperature, humidity, rainfall, and soil potential of hydrogen. After constructing the dataset, preprocessing is the initial step of the proposed framework.Then, feature fusion and selection were performed using the remora-based partial least squares regression method to achieve the best accuracy. Eventually, the most discriminatory features are incorporated into the hybrid ML and DL model known as the extreme learning machine based on the bi-directional long short-term memory for final prediction. The proposed method is implemented in the python platform, and the performance is evaluated in terms of accuracy, precision, recall, F-measure, Kappa, MAE, and log loss. Then, the performance of the proposed method is compared with recent existing methods. As a result, the simulated outcomes proved that the proposed method had achieved better performance than the existing methods.
SummaryAgriculture is the major backbone of India. Therefore, the crop recommendation system is important for the farmers and the country as it reflects the country's economic growth. The crop yield rate is affected due to various parameters such as climatic changes, soil properties, temperature, humidity and so forth. An effective prediction system is required to monitor the field and suggest suitable crops that can provide a maximum yield rate. Therefore, a prediction system is developed in the proposed framework for crop recommendation using the sensor information collected from Maharashtra, India. The dataset has been built with the information collected by 250 sensors located in different Maharashtra places. Initially, the gathered dataset is subjected to preprocessing steps like data cleaning, removing duplicate values, and filling up the missing values. Then, robust and flexible machine learning models like decision tree, random forest, and support vector machine are used, which analyze the preprocessed data and predict the suitable crops that show high yield for a particular area. The implementation of the proposed framework is done using Python. The RF classifier achieved the highest accuracy rate of 95% among the three classifiers.
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